Artificial neural network reproduces human behaviours for the first time
Computer scientists at the Institute for Research in Fundamental Sciences in Tehran, have put together an artificial neural network that mimics the way a monkey's brain recognises faces. They discovered that, not only could the network recognise faces accurately, it also displays many of the idiosyncratic properties of face recognition in humans and monkeys – the inability to recognise faces easily when they are upside down.
MIT Technology Review has the details.
The new neural network consists of six layers with the first four trained to extract primary features. The first two recognise edges, rather like two areas of the visual cortex known as V1 and V2. The next two layers recognise face parts, such as the pattern of eyes, nose and mouth. These layers simulate the behaviour of parts of the brain called V4 and the anterior IT neurons.
The fifth the layer is trained to recognise the same face from different angles. It is known as the view selective layer and inspired by parts of monkey brains called middle face patches.
The final layer matches the face to an identity. This is called the identity selective layer and simulates a part of the simian brain known as the anterior face patch.
No other face recognition system has been able to reproduce biological characteristics of face rejection.
The results suggest that [computer scientist] Amirhossein Farzmahdi and co have found an interesting way to reproduce these human and monkey behaviours in an artificial system for the first time. “Our proposed model…explains neural response characteristics of monkey face patches; as well several behavioral phenomena observed in humans,” they say. ...
If that works for vision, then might it also work for hearing, touch, balance, movement and so on? And beyond that there is the potential for capturing the essence of being human, which must somehow be captured by structures within the brain.